We present an approach to improving automatic speech recognition (ASR) for the creation of medical reports by analyzing hypotheses in the word graph based on background knowledge. Our application area is prescriptions of medications, which are a frequent source of misrecognitions: In a sample of 123 reports, we found that no less than about a third of the active substances or trade names and dosages were recognized incorrectly. In about 25\% of these errors, the correct string of words was contained in the word graph -- a significant potential for improvement. To realize this potential, we have built a knowledge base of medications based on information contained in the Unified Medical Language System (UMLS). This knowledge base contains trade names, active substances, strengths and dosages. Based on this representation, we generate a variety of linguistic realizations for prescriptions. Whenever an inconsistency in a prescription is encountered in the best path of the word graph, the system searches for alternative paths which contain valid linguistic realizations of prescriptions consistent with the knowledge base. If such a path exists, a new concept edge with a better score is added to the word graph, resulting in a higher plausibility for this reading. The concept edge can be used for rescoring the word graph to obtain a new best path. A preliminary evaluation led to encouraging results: in about half of the cases where the word graph contained the correct variant, the correction was successful.